Triple
T16511553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of the Alamo |
E401072
|
entity |
| Predicate | casualtiesMexican |
P34188
|
FINISHED |
| Object | several hundred killed and wounded |
—
|
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: several hundred killed and wounded | Statement: [Battle of the Alamo, casualtiesMexican, several hundred killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesMexican Context triple: [Battle of the Alamo, casualtiesMexican, several hundred killed and wounded]
-
A.
casualtiesMexicanKilled
chosen
Indicates that the event or action resulted in Mexican individuals being killed as casualties.
-
B.
casualtiesTexianKilled
Indicates that the relationship specifies the number of Texian individuals who were killed as casualties in a particular event or conflict.
-
C.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
D.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
E.
battleCommanderMexican
Indicates that one entity serves as the Mexican commander or leading military officer in a particular battle involving the other 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_69d88381f6148190819958a038be990e |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e55fe7c8190abc957bdd326d1bc |
completed | April 18, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69e296995d388190b88ebe189dce890d |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:14 a.m.