Triple
T38580605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Senbon Torii |
E932233
|
entity |
| Predicate | numberOfGatesApprox |
P21387
|
FINISHED |
| Object | thousands |
—
|
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: thousands | Statement: [Senbon Torii, numberOfGatesApprox, thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGatesApprox Context triple: [Senbon Torii, numberOfGatesApprox, thousands]
-
A.
numberOfGates
chosen
Indicates the quantity of gates associated with or belonging to an entity.
-
B.
lengthOfEachGate
Indicates the measurement of the individual length associated with each gate in a set or system.
-
C.
hasBoardingGatesFor
Indicates that a location or facility provides designated boarding gates used for embarking passengers onto specific transportation services (such as flights or trains).
-
D.
hasPassengerBoardingGates
Indicates that an entity is associated with or contains one or more passenger boarding gates used for embarking or disembarking passengers.
-
E.
numberOfGatehouses
Indicates the quantity of gatehouses associated with or present at a given entity or location.
- 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_69f76ec654d48190b421111cf26e54d9 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:32 p.m.