Triple
T1241013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beirut port explosion of 4 August 2020 |
E26656
|
entity |
| Predicate | injuriesApprox |
P25887
|
FINISHED |
| Object | over 6,000 people |
—
|
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 6,000 people | Statement: [Beirut port explosion of 4 August 2020, injuriesApprox, over 6,000 people]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: injuriesApprox Context triple: [Beirut port explosion of 4 August 2020, injuriesApprox, over 6,000 people]
-
A.
injuryType
Indicates the specific kind or category of injury associated with an entity or event.
-
B.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
C.
causeOfInjury
Indicates that one entity is the source or reason that another entity sustained an injury.
-
D.
injuryOccurredAt
Indicates that an injury took place at a specific location or during a particular event or time.
-
E.
injuryInvolvedPlayer
Indicates that a specific player is involved in, affected by, or associated with a particular injury event.
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf4343e48190a232abd8475880a0 |
completed | March 1, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69a4bb696a38819095845c84f0241287 |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bce611ec819092cb13d354d0903e |
completed | March 1, 2026, 10:25 p.m. |
Created at: March 1, 2026, 7:47 p.m.