Triple
T4407099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 5 (O'Hare) |
E93759
|
entity |
| Predicate | hasArrivalsArea |
P38073
|
FINISHED |
| Object | lower level |
—
|
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: lower level | Statement: [Terminal 5 (O'Hare), hasArrivalsArea, lower level]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasArrivalsArea Context triple: [Terminal 5 (O'Hare), hasArrivalsArea, lower level]
-
A.
hasArrivalArea
chosen
Indicates that an entity is associated with a specific area designated for arrivals, such as where incoming people or items first enter or are received.
-
B.
hasWaitingArea
Indicates that an entity provides or includes a designated space where people can wait before receiving a service or proceeding to another area.
-
C.
hasReservationArea
Indicates that an entity is assigned or associated with a specific reserved area or section designated for its use.
-
D.
hasDropOffArea
Indicates that an entity provides a designated area where items, passengers, or goods can be temporarily left or unloaded.
-
E.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
- F. None of above.
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_69b345158c748190a2c040fce2da9980 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b3548b1ca08190b3136867c7098d86 |
completed | March 13, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69b34f5b36a881909bf2e970aa523390 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:28 p.m.