Triple
T30949755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harrisburg, Texas |
E788502
|
entity |
| Predicate | wasBurnedBy |
P60484
|
FINISHED |
| Object | Mexican army |
—
|
NE NERFINISHED |
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: Mexican army | Statement: [Harrisburg, Texas, wasBurnedBy, Mexican army]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasBurnedBy Context triple: [Harrisburg, Texas, wasBurnedBy, Mexican army]
-
A.
burnedDuring
Indicates that one event, object, or process was actively burning or being consumed by fire during the time span of another specified event or interval.
-
B.
burnedWithin
Indicates that one entity was burned while located inside or within the spatial bounds of another entity.
-
C.
burnedDownIn
Indicates that a structure or object was destroyed by fire during a particular event or incident.
-
D.
burned
chosen
Indicates that one entity caused another entity to be damaged or consumed by fire or intense heat.
-
E.
destroyedByFire
Indicates that something has been ruined, damaged, or rendered unusable as a direct result of a fire.
- 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_69f224c180f88190ad177372ee02b7e2 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6953bafb88190a860e9c68a3dd4b2 |
completed | May 3, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69f690ef92308190903a54fc74233269 |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 29, 2026, 8:53 p.m.