Triple
T1234584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal D |
E26517
|
entity |
| Predicate | hasCheckInCounters |
P24791
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Terminal D, hasCheckInCounters, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCheckInCounters Context triple: [Terminal D, hasCheckInCounters, yes]
-
A.
hasCheckInCategory
Indicates that an entity is associated with a specific category or type of check-in event.
-
B.
hasCheckOutTime
Indicates the specific time at which an entity is required or scheduled to check out or depart.
-
C.
hasCountingDirection
Indicates the direction or order in which counting or enumeration proceeds between related entities.
-
D.
hasHeadCount
Indicates that an entity is associated with a specific number of individuals, typically representing the size or count of people (or similar units) related to it.
-
E.
hasHumanPresence
Indicates that humans are physically present in or occupying a given location, object, or context.
- 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_69a4948571c88190a9191e451e6035fd |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be5e421081908f2432528019db25 |
completed | March 1, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69a4bb67d52c8190815d6356b79d6ed5 |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bbf83584819088c69366f58586cc |
completed | March 1, 2026, 10:21 p.m. |
Created at: March 1, 2026, 7:47 p.m.