Triple
T2088455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAN |
E32609
|
entity |
| Predicate | airportElevationFeet |
P34627
|
FINISHED |
| Object | 17 |
—
|
LITERAL 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: 17 | Statement: [SAN, airportElevationFeet, 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportElevationFeet Context triple: [SAN, airportElevationFeet, 17]
-
A.
airportStation
Indicates a location functions as an airport facility where air transport operations occur.
-
B.
hubAirport
Indicates that an airport serves as a primary hub or central operating base for a particular airline or carrier.
-
C.
highestElevationApprox
Indicates that an entity has an approximate value for the maximum elevation reached within its spatial or conceptual extent.
-
D.
isCivilAirport
Indicates that an airport is designated and used primarily for civilian (non-military) aviation operations.
-
E.
largestAirport
Indicates that one airport is the largest (typically by area, traffic, or capacity) among a specified set or within a given region.
- F. None of above. chosen
Provenance (4 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_69a885eba0708190999696a45cbec816 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba712388819091d68a4bb99f6b17 |
completed | March 7, 2026, 5:41 a.m. |
| PD | Predicate disambiguation | batch_69abb7b4356881909217c42ccb8bb1ed |
completed | March 7, 2026, 5:29 a.m. |
| PDg | Predicate description generation | batch_69abb83e7888819096dc40275c77daff |
completed | March 7, 2026, 5:31 a.m. |
Created at: March 4, 2026, 7:43 p.m.