Triple
T3133643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manassas Regional Airport |
E65475
|
entity |
| Predicate | FAA LID |
P420
|
FINISHED |
| Object |
HEF
HEF is the FAA location identifier for Manassas Regional Airport in Virginia, a public airport serving general aviation and regional air traffic.
|
E330566
|
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: HEF | Statement: [Manassas Regional Airport, FAA LID, HEF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HEF Context triple: [Manassas Regional Airport, FAA LID, HEF]
-
A.
HAF
HAF is the commonly used abbreviation for the Hellenic Air Force, the air warfare branch of Greece’s armed forces.
-
B.
HEE
HEE is the acronym for Health Education England, the national body responsible for overseeing education, training, and workforce development for healthcare staff in England.
-
C.
HES
HES is the commonly used abbreviation for Historic Environment Scotland, the public body responsible for protecting and promoting Scotland’s historic environment.
-
D.
HVF
HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
-
E.
Hfd
Hfd is the official station code used to identify Hoofddorp railway station in the Dutch rail network.
- 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: HEF Triple: [Manassas Regional Airport, FAA LID, HEF]
Generated description
HEF is the FAA location identifier for Manassas Regional Airport in Virginia, a public airport serving general aviation and regional air traffic.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HEF Target entity description: HEF is the FAA location identifier for Manassas Regional Airport in Virginia, a public airport serving general aviation and regional air traffic.
-
A.
HAF
HAF is the commonly used abbreviation for the Hellenic Air Force, the air warfare branch of Greece’s armed forces.
-
B.
HEE
HEE is the acronym for Health Education England, the national body responsible for overseeing education, training, and workforce development for healthcare staff in England.
-
C.
HES
HES is the commonly used abbreviation for Historic Environment Scotland, the public body responsible for protecting and promoting Scotland’s historic environment.
-
D.
HVF
HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
-
E.
Hfd
Hfd is the official station code used to identify Hoofddorp railway station in the Dutch rail network.
- 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_69ad8581c25c8190b0d85ba9b9baa531 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada56104ec8190a14591ed73f3fe83 |
completed | March 8, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f84d8288190b1f48fa0f5c10773 |
completed | March 12, 2026, 12:57 a.m. |
| NEDg | Description generation | batch_69b2102e35b08190ad9ca397f0c937da |
completed | March 12, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b21458b07081909d75886e0d9f88e9 |
completed | March 12, 2026, 1:18 a.m. |
Created at: March 8, 2026, 3:05 p.m.