Triple
T19038216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Longues-sur-Mer battery |
E465931
|
entity |
| Predicate | hasOriginalGuns |
P133653
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Longues-sur-Mer battery, hasOriginalGuns, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOriginalGuns Context triple: [Longues-sur-Mer battery, hasOriginalGuns, true]
-
A.
hasOriginalMecha
Indicates that an entity is associated with its initial or primary mecha design, model, or unit from which others may derive or be based.
-
B.
numberOfGuns
Indicates the quantity of guns associated with a given entity or situation.
-
C.
hasSmallCalibreGuns
Indicates that the subject is equipped with or possesses guns of relatively small calibre compared to standard or typical armaments.
-
D.
originallyHad
Indicates that an entity previously possessed, contained, or was associated with something before a change, loss, or transformation occurred.
-
E.
hasHumanBuiltWeapon
Indicates that an entity possesses, controls, or is equipped with a weapon that has been constructed or manufactured by humans.
- 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_69d8dd0359648190bc2a9202c5cf29d2 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d7fed99c81909495797c604db044 |
completed | April 20, 2026, 7:38 a.m. |
| PD | Predicate disambiguation | batch_69e4a3001e388190aa6057266514e75a |
completed | April 19, 2026, 9:40 a.m. |
| PDg | Predicate description generation | batch_69e4ad8f6f7c8190af645bf08823ee2b |
completed | April 19, 2026, 10:25 a.m. |
Created at: April 10, 2026, 12:02 p.m.