Triple
T28431087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Would I Lie to You? |
E715129
|
entity |
| Predicate | typicalNumberOfCaptains |
P202631
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Would I Lie to You?, typicalNumberOfCaptains, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfCaptains Context triple: [Would I Lie to You?, typicalNumberOfCaptains, 2]
-
A.
captainType
Indicates the specific role or category of captain associated with an entity within a given context or system.
-
B.
crewCount
Indicates the number of crew members associated with an entity, such as a vehicle, vessel, or mission.
-
C.
crewCountApproximate
Indicates that the relationship specifies an estimated or approximate number of crew members associated with an entity.
-
D.
captainServedUnder
Indicates that one captain performed their service or held their position under the authority or command of another person.
-
E.
captainInName
Indicates that an entity’s name or title explicitly includes the word “Captain.”
- 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_69efd6b253888190b3c7222ed6a403a8 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_6a00a2f0d1588190a936ea7df0ef0464 |
completed | May 10, 2026, 3:23 p.m. |
| PD | Predicate disambiguation | batch_6a00a28ccd94819085b5e123f5a4769e |
completed | May 10, 2026, 3:21 p.m. |
| PDg | Predicate description generation | batch_6a00a2f0207c81908eaaff8c2a0b3f97 |
completed | May 10, 2026, 3:23 p.m. |
Created at: April 28, 2026, 1:39 a.m.