Triple
T33190957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramzi Yousef |
E849608
|
entity |
| Predicate | injuryCountInAttack |
P25887
|
FINISHED |
| Object | over 1000 injured in 1993 World Trade Center bombing |
—
|
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 1000 injured in 1993 World Trade Center bombing | Statement: [Ramzi Yousef, injuryCountInAttack, over 1000 injured in 1993 World Trade Center bombing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: injuryCountInAttack Context triple: [Ramzi Yousef, injuryCountInAttack, over 1000 injured in 1993 World Trade Center bombing]
-
A.
numberOfAttacks
Indicates the count of distinct attack events associated with a given entity or interaction.
-
B.
hasApproximateNumberOfWounds
Indicates that an entity has a number of wounds that is known only approximately rather than as an exact count.
-
C.
numberOfVictimsInjured
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
D.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
-
E.
injuriesApprox
chosen
Indicates an approximate or estimated number or extent of injuries associated with an event or 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_69f3495e0f108190a6a7006f79f9c2c3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6dd3cc0648190a275812d6711275a |
completed | May 3, 2026, 5:29 a.m. |
| PD | Predicate disambiguation | batch_69f6d82eaee081908f06a71546315aea |
completed | May 3, 2026, 5:07 a.m. |
Created at: May 1, 2026, 1:29 a.m.