Triple
T18979458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hallabong tangerine |
E464380
|
entity |
| Predicate | hasSkinThickness |
P2073
|
FINISHED |
| Object | relatively thick peel |
—
|
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: relatively thick peel | Statement: [Hallabong tangerine, hasSkinThickness, relatively thick peel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSkinThickness Context triple: [Hallabong tangerine, hasSkinThickness, relatively thick peel]
-
A.
skinThickness
chosen
Indicates the measured thickness of an entity’s skin, typically quantifying how thick its outer tissue layer is.
-
B.
bodyThickness
Indicates the measured or relative thickness of an entity’s body in the context of a comparison or description.
-
C.
isThickenedWith
Indicates that one substance has been made more viscous or dense by adding another substance that serves as a thickening agent.
-
D.
hasMaximumThickness
Indicates that an entity possesses a specified upper limit on its thickness.
-
E.
archLength
Indicates the measured length of an arch-shaped structure or path between two defined points.
- 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_69d8dd008af48190a97ff1c6488edf1b |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d65b573881908575e61a62b70787 |
completed | April 20, 2026, 7:31 a.m. |
| PD | Predicate disambiguation | batch_69e4a2f437648190b85650dae8885d48 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 12:01 p.m.