Triple
T9528147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KK Women's and Children's Hospital |
E229814
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | KKH |
E409908
|
NE FINISHED |
How this triple was built (2 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: KKH | Statement: [KK Women's and Children's Hospital, shortName, KKH]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KKH Context triple: [KK Women's and Children's Hospital, shortName, KKH]
-
A.
KKH
chosen
KKH is the abbreviation for the Karakoram Highway, a major high-altitude road linking Pakistan and China through the Karakoram mountain range.
-
B.
KHH
KHH is the IATA airport code for Kaohsiung International Airport, a major airport serving Kaohsiung in southern Taiwan.
-
C.
KCH
KCH is the vehicle registration code assigned to the town of Chrzanów in southern Poland.
-
D.
KHC
KHC is a selective interdisciplinary honors college at Boston University that offers an enriched curriculum and close-knit academic community for high-achieving undergraduates.
-
E.
KTXH
KTXH is a Houston-based television station that serves the local market with syndicated and network programming.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca8479934c81908006d0e6e970ae05 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98b0466081908eb637e2185dea37 |
completed | April 1, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d14c30c6008190b2eff99d74f18070 |
completed | April 4, 2026, 5:36 p.m. |
Created at: March 30, 2026, 8 p.m.