Triple
T33199526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chaiyya Chaiyya |
E849860
|
entity |
| Predicate | hasTrainSequence |
P199232
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Chaiyya Chaiyya, hasTrainSequence, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainSequence Context triple: [Chaiyya Chaiyya, hasTrainSequence, true]
-
A.
hasTailTrain
Indicates that one entity possesses or is characterized by a tail-like train extending from it.
-
B.
hasPlannedTrain
Indicates that an entity is associated with a train that is scheduled or planned to operate, rather than one currently in service.
-
C.
hasTrainIdentification
Indicates that an entity is associated with a specific train identification code or number used to uniquely identify that train.
-
D.
hasTrainStyle
Indicates that one entity (typically a train or rail service) is characterized by or associated with a particular style, type, or configuration of train.
-
E.
hasLNGTrain
Indicates that something possesses or is equipped with an LNG (liquefied natural gas) processing or transport train as part of its facilities or infrastructure.
- 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_69f3495efedc8190843a5728089544b9 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ff27b125948190aced0fe0189fd39a |
completed | May 9, 2026, 12:25 p.m. |
| PD | Predicate disambiguation | batch_69ff26c30a0481909ef6a54ded851e42 |
completed | May 9, 2026, 12:21 p.m. |
| PDg | Predicate description generation | batch_69ff27b0385c8190bccf340d26268f77 |
completed | May 9, 2026, 12:25 p.m. |
Created at: May 1, 2026, 1:29 a.m.