Triple
T31013285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | al-Bab |
E790261
|
entity |
| Predicate | battlegroundFor |
P170909
|
FINISHED |
| Object | Turkey |
—
|
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: Turkey | Statement: [al-Bab, battlegroundFor, Turkey]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: battlegroundFor Context triple: [al-Bab, battlegroundFor, Turkey]
-
A.
battlegroundType
Indicates the specific kind or category of environment in which a battle or conflict takes place.
-
B.
battlefieldOf
Indicates that a location is the site where a particular battle or military engagement took place.
-
C.
wasKeyBattlegroundDuring
Indicates that a location served as an important site of conflict or struggle during a specified event or period.
-
D.
wasBattlegroundRace
Indicates that a particular race or ethnic group was a primary party or side in a conflict or struggle over control, influence, or dominance in a given context.
-
E.
battleAlsoKnownAs
Indicates that a particular battle is known by an alternative name or names.
- 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_69f224c73ca48190a1e46cb58ad4045b |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f695f9fe7c819084322bf6cdc70a13 |
completed | May 3, 2026, 12:25 a.m. |
| PD | Predicate disambiguation | batch_69f690ef92308190903a54fc74233269 |
completed | May 3, 2026, 12:03 a.m. |
| PDg | Predicate description generation | batch_69f695385a2881908cc28ef97fffc867 |
completed | May 3, 2026, 12:22 a.m. |
Created at: April 29, 2026, 8:57 p.m.