Triple
T10817741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hyder Ali |
E255275
|
entity |
| Predicate | allegianceAtStartOfCareer |
P1201
|
FINISHED |
| Object | Mysore army |
—
|
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: Mysore army | Statement: [Hyder Ali, allegianceAtStartOfCareer, Mysore army]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: allegianceAtStartOfCareer Context triple: [Hyder Ali, allegianceAtStartOfCareer, Mysore army]
-
A.
hadAllegiance
Indicates that an entity was loyally committed or formally bound in support or service to another entity, such as a person, group, or cause.
-
B.
countryOfAllegiance
Indicates the country to which an entity owes loyalty, support, or official allegiance.
-
C.
leagueDebutCountry
Indicates the country in which an entity (typically an athlete or team) made its debut in a particular league.
-
D.
spentMostOfCareerIn
Indicates that an individual devoted the majority of their professional working life to being in or associated with a particular place, organization, or context.
-
E.
allegiance
chosen
Indicates a relationship where one entity is loyal, committed, or obligated in support or service to another entity.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7344866f88190be4addb7c8020fce |
completed | April 9, 2026, 5:08 a.m. |
| PD | Predicate disambiguation | batch_69d70d1bf3648190b36fa96ea018e0dc |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:18 p.m.