Triple
T5845768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alan Black |
E129705
|
entity |
| Predicate | workplaceAtTimeOfAttack |
P66679
|
FINISHED |
| Object | textile factory in Glenanne |
—
|
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: textile factory in Glenanne | Statement: [Alan Black, workplaceAtTimeOfAttack, textile factory in Glenanne]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workplaceAtTimeOfAttack Context triple: [Alan Black, workplaceAtTimeOfAttack, textile factory in Glenanne]
-
A.
workedAs
Indicates that an entity held a particular job, role, or position, performing work in that capacity.
-
B.
officeAttacked
Indicates that an office was the target of an attack or violent action.
-
C.
victimOccupation
Indicates the profession or job role held by the person who is the victim in an event or incident.
-
D.
defendantOccupationAtTime
Indicates that a defendant held a particular occupation or job role during a specified time period.
-
E.
antagonistWorkplace
Indicates that the antagonist is associated with or operates within a particular workplace or professional environment.
- 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_69c0084bd31c8190a796bb6284845e83 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03c9239e08190bff7ef2bd6d21ae0 |
completed | March 22, 2026, 7:01 p.m. |
| PD | Predicate disambiguation | batch_69c0334412388190bc594794ec5754f9 |
completed | March 22, 2026, 6:21 p.m. |
| PDg | Predicate description generation | batch_69c03c8d579081909d7b97fc9014b5d7 |
completed | March 22, 2026, 7:01 p.m. |
Created at: March 22, 2026, 3:55 p.m.