Triple
T27631542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luis Urzúa |
E696348
|
entity |
| Predicate | numberOfPeopleSupervisedDuringAccident |
P198647
|
FINISHED |
| Object | 33 |
—
|
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: 33 | Statement: [Luis Urzúa, numberOfPeopleSupervisedDuringAccident, 33]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleSupervisedDuringAccident Context triple: [Luis Urzúa, numberOfPeopleSupervisedDuringAccident, 33]
-
A.
numberOfVictimsInjured
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
B.
numberOfPeopleTrapped
Indicates the count of individuals who are currently trapped in a given situation or location.
-
C.
hasInjuredPerson
Indicates that an entity has a person who has been harmed or injured associated with it.
-
D.
numberOfRescuers
Indicates the quantity of rescuers involved in or assigned to a particular rescue-related situation or event.
-
E.
numberOfPeopleLaterDyingOfInjuriesConsidered
Indicates the number of people who subsequently died from injuries that were previously evaluated or taken into account.
- 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_69ef59092c8881908114ad184248cc46 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69fefa064ab48190925759950d0d94d9 |
completed | May 9, 2026, 9:10 a.m. |
| PD | Predicate disambiguation | batch_69fef96ae5d08190b027435753c44821 |
completed | May 9, 2026, 9:07 a.m. |
| PDg | Predicate description generation | batch_69fefa05757481908fa38f5c604afbbe |
completed | May 9, 2026, 9:10 a.m. |
Created at: April 27, 2026, 2:21 p.m.