Triple
T5260607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Ai |
E118812
|
entity |
| Predicate | numberOfInitialAttackers |
P41216
|
FINISHED |
| Object | about 3,000 men (biblical account) |
—
|
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: about 3,000 men (biblical account) | Statement: [Battle of Ai, numberOfInitialAttackers, about 3,000 men (biblical account)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInitialAttackers Context triple: [Battle of Ai, numberOfInitialAttackers, about 3,000 men (biblical account)]
-
A.
numberOfAttacks
Indicates the count of distinct attack events associated with a given entity or interaction.
-
B.
attackerStrength
Indicates the level or magnitude of power, force, or capability possessed by the attacking entity in a given interaction or scenario.
-
C.
numberOfInvaders
Indicates the quantity of entities classified as invaders associated with a given subject or context.
-
D.
attackingForceApprox
Indicates that one entity is the approximate attacking force, in terms of size or strength, relative to another entity or context.
-
E.
numberOfForcesAtStart
chosen
Indicates the quantity of forces present or applied at the initial point or beginning of a process, event, or scenario.
- 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_69bd446a42c88190b7ecbef006561d55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7bcf026c8190881b6e14b962a3c9 |
completed | March 20, 2026, 4:54 p.m. |
| PD | Predicate disambiguation | batch_69bd77c55224819096c0bcfcfae79bd3 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:50 p.m.