Triple
T36835601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henschel turret for Tiger II |
E910262
|
entity |
| Predicate | turretRearArmorThickness |
P186719
|
FINISHED |
| Object | 80 mm |
—
|
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: 80 mm | Statement: [Henschel turret for Tiger II, turretRearArmorThickness, 80 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: turretRearArmorThickness Context triple: [Henschel turret for Tiger II, turretRearArmorThickness, 80 mm]
-
A.
turretArmorType
Indicates the type or classification of armor used on a turret in the relationship.
-
B.
armorTurretFaceThickness
Indicates the thickness of the armor on the front-facing surface of a turret.
-
C.
mainBatteryTurretArmor
Indicates the thickness or protective characteristics of the armor on a vessel’s primary gun turrets.
-
D.
turret
Indicates that an entity is equipped with or associated with a turret, typically a rotating weapon or defense mechanism.
-
E.
frontHullArmorThickness
Indicates the thickness of the armor located on the front section of a vehicle’s hull.
- 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_69f76e7e9d60819092442fba73290a46 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fa0a7b00948190a257273d9968c5d7 |
completed | May 5, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69f9fec9c9488190ae2a349651a02782 |
completed | May 5, 2026, 2:29 p.m. |
| PDg | Predicate description generation | batch_69fa0a799b9081909bfa8293a22c4b00 |
completed | May 5, 2026, 3:19 p.m. |
Created at: May 3, 2026, 4:13 p.m.