Triple
T611706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bir-Hakeim |
E12112
|
entity |
| Predicate | hasPassengerInformationSystem |
P17090
|
FINISHED |
| Object | electronic displays |
—
|
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: electronic displays | Statement: [Bir-Hakeim, hasPassengerInformationSystem, electronic displays]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPassengerInformationSystem Context triple: [Bir-Hakeim, hasPassengerInformationSystem, electronic displays]
-
A.
hasBaggageSystem
Indicates that an entity is equipped with or utilizes a baggage handling system.
-
B.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
C.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
-
D.
hasOnboardComputer
Indicates that one entity is equipped with or contains an onboard computer system.
-
E.
hasPassengerUsageCategory
Indicates the classification of how a passenger-related resource or service is used (e.g., its usage type or category for passengers).
- 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_69a493309df48190a327f748e88049a6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49e07739481909930a6577c081b9e |
completed | March 1, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69a49cfa7b4481909bec7a5fd3e98c65 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49def31ec81909dc53e70f4a36eda |
completed | March 1, 2026, 8:13 p.m. |
Created at: March 1, 2026, 7:35 p.m.