Triple
T36576652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hani Hanjour |
E902272
|
entity |
| Predicate | numberOfPeopleKilledAtPentagon |
P204945
|
FINISHED |
| Object | 125 |
—
|
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: 125 | Statement: [Hani Hanjour, numberOfPeopleKilledAtPentagon, 125]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleKilledAtPentagon Context triple: [Hani Hanjour, numberOfPeopleKilledAtPentagon, 125]
-
A.
numberOfHostagesKilled
Indicates the number of hostages who were killed in the context of a specific event or situation.
-
B.
numberOfPeopleKilledInBombing
Indicates the total count of people who were killed as a direct result of a specific bombing event.
-
C.
numberOfPeopleReportedKilled
Indicates the reported count of people who have been killed in an incident or event.
-
D.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
-
E.
numberOfPeopleKilledOnGround
Indicates the count of people who were killed on the ground as a result of the event or incident.
- 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_69f76e64d8908190868473959a250b94 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:11 p.m.