Triple
T10949802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014 Mackintosh Building fire |
E258695
|
entity |
| Predicate | numberOfFirefightersInvolved |
P96794
|
FINISHED |
| Object | over 120 |
—
|
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: over 120 | Statement: [2014 Mackintosh Building fire, numberOfFirefightersInvolved, over 120]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFirefightersInvolved Context triple: [2014 Mackintosh Building fire, numberOfFirefightersInvolved, over 120]
-
A.
numberOfRescuers
Indicates the quantity of rescuers involved in or assigned to a particular rescue-related situation or event.
-
B.
fireRescue
Indicates a relationship where one entity performs or is responsible for rescuing people or property from fires or fire-related emergencies involving another entity.
-
C.
numberOfTroopsInvolved
Indicates the quantity of military personnel participating in or assigned to a specific operation, event, or engagement.
-
D.
fireIncidentRelated
Indicates that there is a connection or association between a specific fire incident and another entity, event, or record.
-
E.
numberOfPeopleTrapped
Indicates the count of individuals who are currently trapped in a given situation or location.
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770ed2f1c819081ec58457f57889d |
completed | April 9, 2026, 9:27 a.m. |
| PD | Predicate disambiguation | batch_69d72e816a98819096d6c10dfb88a66a |
completed | April 9, 2026, 4:43 a.m. |
| PDg | Predicate description generation | batch_69d7322370648190ba14cdd6fb4cdcb0 |
completed | April 9, 2026, 4:59 a.m. |
Created at: April 8, 2026, 9:23 p.m.