Triple
T499378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hana, Maui |
E10365
|
entity |
| Predicate | airportCode |
P418
|
FINISHED |
| Object |
HNM
HNM is the IATA airport code for Hana Airport, a small regional airport serving the town of Hana on the island of Maui in Hawaii.
|
E62142
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: HNM | Statement: [Hana, Maui, airportCode, HNM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HNM Context triple: [Hana, Maui, airportCode, HNM]
-
A.
HM
HM is an abbreviation commonly used as a formal title for a reigning queen or king, standing for "Her Majesty" or "His Majesty."
-
B.
HMG
HMG is the common abbreviation for His Majesty’s Government, the central executive authority of the United Kingdom responsible for national policy and administration.
-
C.
HMT
HMT is the commonly used abbreviation for HM Treasury, the United Kingdom government department responsible for economic and financial policy.
-
D.
Hart
Hart is a surname most famously associated with Moss Hart, the acclaimed American playwright and theater director known for works like "You Can't Take It with You" and "Once in a Lifetime."
-
E.
NAM
NAM is the commonly used acronym for the National Academy of Medicine, a leading U.S. nonprofit institution that provides expert advice on health, medicine, and biomedical science.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: HNM Triple: [Hana, Maui, airportCode, HNM]
Generated description
HNM is the IATA airport code for Hana Airport, a small regional airport serving the town of Hana on the island of Maui in Hawaii.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HNM Target entity description: HNM is the IATA airport code for Hana Airport, a small regional airport serving the town of Hana on the island of Maui in Hawaii.
-
A.
HM
HM is an abbreviation commonly used as a formal title for a reigning queen or king, standing for "Her Majesty" or "His Majesty."
-
B.
HMG
HMG is the common abbreviation for His Majesty’s Government, the central executive authority of the United Kingdom responsible for national policy and administration.
-
C.
HMT
HMT is the commonly used abbreviation for HM Treasury, the United Kingdom government department responsible for economic and financial policy.
-
D.
Hart
Hart is a surname most famously associated with Moss Hart, the acclaimed American playwright and theater director known for works like "You Can't Take It with You" and "Once in a Lifetime."
-
E.
NAM
NAM is the commonly used acronym for the National Academy of Medicine, a leading U.S. nonprofit institution that provides expert advice on health, medicine, and biomedical science.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f119b14c8190a5a6b119579c2682 |
completed | Feb. 28, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a481f1ee28819087e90028b89e877e |
completed | March 1, 2026, 6:14 p.m. |
| NEDg | Description generation | batch_69a482749e78819093f8f81090eedfa2 |
completed | March 1, 2026, 6:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a483073dcc8190b05f89a81207d3d2 |
completed | March 1, 2026, 6:18 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.