Triple
T10277087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leicester Panthers |
E240994
|
entity |
| Predicate | hasNotableFormerMemberOccupation |
P304
|
FINISHED |
| Object | NFL head coach |
—
|
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: NFL head coach | Statement: [Leicester Panthers, hasNotableFormerMemberOccupation, NFL head coach]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFormerMemberOccupation Context triple: [Leicester Panthers, hasNotableFormerMemberOccupation, NFL head coach]
-
A.
hasFormerStaffMember
Indicates that an entity once had a person as a staff member, but that person is no longer employed there.
-
B.
hasNotableMember
chosen
Indicates that a group, organization, or collection includes at least one member who is distinguished or noteworthy in some significant way.
-
C.
formerMemberOf
Indicates that an entity once belonged to or was affiliated with a group, organization, or body, but is no longer a member.
-
D.
notableFormerHolderRole
Indicates that an entity previously held a particular notable role or position.
-
E.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ccb7ec8190a538cf279e48116e |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f117708190928f92ae2611d724 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:37 a.m.